Spurious measurements in surface data are common in technical surfaces. Excluding or ignoring these spurious points may lead to incorrect surface characterization if these points inherit features of the surface. Therefore, data imputation must be applied to ensure that the estimated data points at spurious measurements do not strongly deviate from the true surface and its characteristics. Traditional surface data imputation methods rely on simple assumptions and ignore existing knowledge of the surface, yielding in suboptimal estimates. In this paper, we propose using stochastic processes for data imputation. This approach, which originates from surface simulation, allows for the straightforward integration of a priori knowledge. We employ Gaussian processes for surface data imputation with artificially generated missing features. Both the surfaces and the missing features are generated artificially. Our results demonstrate that the proposed method fills the missing values and interpolates data points with better alignment to the measured surface compared to traditional approaches, particularly when surface features are missing.

翻译：在技术表面中，表面数据中的异常测量值十分常见。若这些异常点继承了表面的特征，排除或忽略它们可能导致错误的表面表征。因此，必须应用数据插补技术，以确保在异常测量位置估计的数据点不会严重偏离真实表面及其特性。传统的表面数据插补方法依赖于简单假设，并忽略了表面的现有知识，导致估计结果欠佳。本文提出使用随机过程进行数据插补。该方法源于表面模拟，能够直接整合先验知识。我们采用高斯过程对含有人工生成缺失特征的表面数据进行插补。表面和缺失特征均为人造生成。实验结果表明，与传统方法相比，所提方法能够填补缺失值并以更贴合测量表面的方式插补数据点，尤其在表面特征缺失的情况下表现更优。